International Journal of Science, Engineering and TechnologyAuthors: Sudhir Vishnubhatla
Abstract: Regulatory and compliance operations generate vast volumes of complex legal, supervisory, and financial documentation, which must be accurately categorized to support functions such as supervisory reporting, real-time risk monitoring, and external audits. Historically, this classification relied on manual review and brittle rule-based systems, leading to high operational costs, lagging turnaround times, and uneven quality. By 2018, rapid advances in natural language processing (NLP) fundamentally reshaped this landscape. Distributed word representations such as word2vec and GloVe, neural network architectures for text classification, and scalable cloud-based ingestion platforms made it possible to automate classification workflows with far greater speed, consistency, and adaptability than traditional methods. This article examines the progression from early feature-based machine learning approaches to modern neural classification frameworks, particularly in the context of regulatory corpora like JRC-Acquis, EuroVoc, and SEC filings. We highlight the key architectural components including ingestion pipelines, streaming frameworks, and classification engines that collectively enable contemporary compliance automation.
DOI: http://doi.org/10.5281/zenodo.17473977
